Data Characteristics in Pharmacovigilance
Pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), post-market surveillance systems (e.g., FDA Adverse Event Reporting System, FAERS; European Medicines Agency EudraVigilance), medical literature, and spontaneous patient reports. This data combines structured formats (e.g., database records with patient demographics, drug information, adverse event codes, event onset and outcome times) and unstructured formats (e.g., free-text case reports, medical notes, email communications). Data updates are frequent, especially during new drug launches and critical monitoring periods, leading to rapid data growth. Documents often include Case Report Forms (CRFs), medical reviews, and regulatory submission files. These documents contain extensive specialized terminology, abbreviations, and specific coding systems (e.g., MedDRA, WHO-ART). Field types are diverse, including qualitative descriptions (adverse event names, severity) and quantitative metrics (dosage, frequency, lab results). Units require strict standardization to prevent confusion.
Constraints on Multi-turn Conversations and Prompts
The multi-source and complex nature of pharmacovigilance data demands high semantic understanding and context management from multi-turn dialogue systems. The specialized terminology from coding systems like MedDRA requires precise prompt design to prevent the model from misinterpreting or over-generalizing adverse event descriptions. Frequent data updates mean the knowledge base needs regular synchronization to ensure multi-turn conversations use the latest information. Free-text descriptions in unstructured data require the model to extract key information from lengthy, highly specialized text and integrate it into structured queries. Multi-turn conversations must handle user questions of varying granularity, from broad drug risk overviews to specific case details. This requires prompts to flexibly adjust focus when guiding the model. Additionally, multi-turn conversations need to identify and correct potential user medical knowledge biases, which prompt design must consider for error correction mechanisms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 tokens | Retains sufficient context in pharmacovigilance conversations to understand complex cases and the logical relationships in multi-turn follow-up questions. |
temperature | 0.3 | Ensures accuracy and consistency of model output, preventing hallucinations in the highly specialized pharmacovigilance domain. |
Recall count | 10 | Recalls enough relevant adverse event reports or drug information from the knowledge base to increase information coverage. |
Similarity threshold | 0.78 | Balances the relevance and quantity of recall results, filtering out low-relevance information to focus on core issues. |
Chunk size | 500 characters | Accommodates long descriptions in case reports, ensuring each segment contains complete semantic meaning and reduces information truncation. |
Rerank result count | 5 | Selects the most relevant entries from recall results, prioritizing their presentation to the user, improving information retrieval efficiency. |
Common Pitfalls
- The model misidentifies specific adverse events, for example, over-generalizing "abnormal liver function" to "digestive system problems." This happens when prompts provide insufficient constraints on specialized medical terminology or when training data lacks sufficient coverage of relevant cases.
- In multi-turn conversations, when a user asks for detailed information about an adverse event, the model cannot locate the specific case mentioned in earlier turns. This occurs when
maxContextis set too low, causing the model to lose context from earlier conversation turns. - A user inputs a drug name and an adverse event, but the system returns "no relevant information found." This can be due to an outdated knowledge base or a failure to correctly process drug name aliases or spelling variations during tokenization and indexing.
Validation Steps
- Input a series of case descriptions containing common adverse events. Check if the model accurately identifies and classifies the adverse events. Compare them against predefined MedDRA codes to confirm coding consistency thresholds.
- Conduct multi-turn conversation tests, starting with a drug risk overview and gradually delving into specific adverse event details. Check if the model maintains contextual coherence and provides relevant information based on follow-up questions.
- Query using drug names and adverse event terms with different spellings, abbreviations, or synonyms. Confirm the system correctly matches relevant entries in the knowledge base. Check the distribution of relevance scores for recalled results.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.